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John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

In many of our Meaningful Use Monday series we focused on a lot of the details around the meaningful use regulations. In this post I want to highlight one of the strategies that I’ve seen a bunch of EHR vendors and other EHR related companies employing to meet Meaningful Use. It’s an interesting concept that will be exciting to see play out.

The idea is what many are calling clinical data abstraction. I’ve actually heard some people refer to it as other names as well, but clinical data abstraction is the one that I like most.

I’ve seen two main types of clinical data abstraction. One is the automated clinical data abstraction. The other is manual clinical data abstraction. The first type is where your computer or server goes through the clinical content and using some combination of natural language processing (NLP) or other technology it identifies the important clinical data elements in a narrative passage. The second type is where a trained medical professional pulls out the various clinical data elements.

I asked one vendor that is working on clinical data abstraction whether they thought that the automated, computer generated clinical abstraction would be the predominate means or whether some manual abstraction will always be necessary. They were confident that we could get there with the automated computer abstraction of the clinical data. I’m not so confident. I think like transcription the computer could help speed up the abstraction, but there might still need to be someone who checks and verifies the data abstraction.

Why does this matter for meaningful use?
One of the challenges for meaningful use is that it really wants to know that you’ve documented certain discrete data elements. It’s not enough for you to just document the smoking status in a narrative paragraph. You have to not only document the smoking status, but your EMR has to have a way to report that you have documented the various meaningful use measures. In comes clinical data abstraction.

Proponents of clinical data abstraction argue that clinical data abstraction provides the best of both worlds: narrative with discrete data elements. It’s an interesting argument to make since many doctors love to see and read the narrative. However, all indications are that we need discrete data elements in order to improve patient care and see some of the other benefits of capturing all this healthcare data. In fact, the future Smart EMR that I wrote about before won’t be possible without these discrete healthcare data elements.

So far I believe that most people who have shown meaningful use haven’t used clinical data abstraction to meet the various meaningful use measures. Although, it’s an intriguing story to tell and could be an interesting way for doctors to meet meaningful use while minimizing changes to their workflow.

Side Note: Clinical data abstraction is also becoming popular when scanning old paper charts into your EHR. Although, that’s a topic for a future post.

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